library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)

theme_cowplot2 <- function(...) {
  theme_cowplot(font_size = 12, ...) %+replace%
    theme(strip.background = element_blank(),
          plot.background = element_blank())
}
theme_set(theme_cowplot2())
coi <- params$cell_type
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
louvain_cluster <- params$louvain_cluster

1 Cluster markers

1.1 Major Ovarian.cancer.super markers for cell assign

### load all data ---------------------------------
source("_src/global_vars.R")

# seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Ovarian.cancer.super_highqc.rds"))

myfeatures <- c("umapharmony_1", "umapharmony_2", "sample", louvain_cluster, "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")

plot_data_wrapper <- function(cluster_res) {
  cluster_res <- enquo(cluster_res)
  as_tibble(FetchData(seu_obj, myfeatures)) %>% 
    left_join(meta_tbl, by = "sample") %>% 
    rename(cluster = !!cluster_res) %>% 
    mutate(cluster = as.character(cluster),
           tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
}

plot_data <- plot_data_wrapper(louvain_cluster)

1.2 Subtype currated markers

helper_f <- function(x) ifelse(is.na(x), "", x)

markers_v7_super[[coi]] %>% 
  group_by(subtype) %>% 
  mutate(rank = row_number(gene)) %>% 
  spread(subtype, gene) %>% 
  mutate_all(.funs = helper_f) %>% 
  formattable::formattable()

rank	Cancer.cell.1	Cancer.cell.2	Cancer.cell.3	Cancer.cell.4	Cancer.cell.5	Cancer.cell.6	Ciliated.cell.1	Ciliated.cell.2	Ciliated.cell.3	Cycling.cancer.cell.1	Cycling.cancer.cell.2	doublet.Immune.cell
1	DAPL1	IGFBP3	CXCL10	COL1A1	CCND1	FTL	C20orf85	C5orf49	ATP5F1E	CENPF	ATAD2	CD52
2	FOLR1	KRT17	IFI6	COL1A2	CRISP3	HSPA6	CAPS	CFAP126	DEFB1	HMGB2	H2AFZ	IGHG1
3	SCGB1D2	KRT7	IFIT1	COL3A1	OVGP1	MGP	CAPSL	DNALI1	GNAS	MKI67	HIST1H4C	IGKC
4	SCGB2A1	LCN2	IFIT2	DCN	PLCG2	RACK1	PIFO	PIFO	GPX3	PTTG1	PCLAF	IGLC3
5	SNHG19	MMP7	IFIT3	FN1	PTMS	SCX	TPPP3	RSPH1	IMPA2	TOP2A	TUBA1B	IL7R
6		S100A10	ISG15	SPARC	SNHG9	ZFAS1						PTPRC
7		S100A9	MX1
8		SLPI
9		TACSTD2

1.3 Subtype cluster markers

## define patient specific clusters
patient_specific_clusters <- plot_data %>% 
  group_by(patient_id_short, cluster) %>% 
  tally %>% 
  group_by(cluster) %>% 
  mutate(nrel = n / sum(n),
         ntotal = sum(n)) %>% 
  arrange(desc(nrel)) %>% 
  distinct(cluster, .keep_all = T) %>% 
  filter(nrel > 0.5 & n < 2000) %>% 
  ungroup %>% 
  mutate(cluster_label_ps = make.names(paste0("OV.", patient_id_short, ".specific"), unique = T),
         cluster = as.numeric(cluster)) %>% 
  distinct(cluster, cluster_label_ps)


## Hypergeometric test --------------------------------------

# marker_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Ovarian.cancer.super_highqc_markers_02.tsv")
marker_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Ovarian.cancer.super_highqc_markers_03.tsv")

test_set <- marker_tbl %>% 
  group_by(cluster) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(k = length(cluster)) %>% 
  ungroup %>%
  select(cluster, gene, k) %>% 
  mutate(join_helper = 1) %>% 
  group_by(cluster, join_helper, k) %>%
  nest(test_set = gene)

markers_doub_tbl <- markers_v7 %>% 
  enframe("subtype", "gene") %>% 
  filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>% 
  unnest(gene) %>% 
  group_by(gene) %>% 
  filter(length(gene) == 1) %>% 
  mutate(subtype = paste0("doublet.", subtype)) %>% 
  bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))

ref_set <- markers_v7_super[[coi]] %>% 
  bind_rows(markers_doub_tbl) %>% 
  group_by(subtype) %>% 
  mutate(m = length(gene),
         n = length(rownames(seu_obj))-m,
         join_helper = 1) %>% 
  group_by(subtype, m, n, join_helper) %>%
  nest(ref_set = gene)

hyper_tbl <- test_set %>% 
  left_join(ref_set, by = "join_helper") %>% 
  group_by(cluster, subtype, m, n, k) %>%
  do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
  mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
  ungroup %>%
  mutate(qval = p.adjust(pval, "BH"),
         sig = qval < 0.01)

# hyper_tbl %>% 
#   group_by(subtype) %>% 
#   filter(any(qval < 0.01)) %>%
#   ggplot(aes(subtype, -log10(qval), fill = sig)) +
#   geom_bar(stat = "identity") +
#   facet_wrap(~cluster) +
#   coord_flip()
  
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet|dissociated")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))

cluster_label_tbl <- hyper_tbl %>% 
  mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>% 
  arrange(qval, subtype) %>%
  group_by(cluster) %>% 
  slice(1) %>% 
  mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>% 
  select(cluster, cluster_label = subtype) %>% 
  ungroup %>% 
  mutate(cluster_label = make.unique(cluster_label, sep = "_")) %>% 
  left_join(patient_specific_clusters, by = "cluster") %>% 
  mutate(cluster_label = ifelse(is.na(cluster_label_ps), cluster_label, cluster_label_ps)) %>% 
  select(-cluster_label_ps)

seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label

cluster_n_tbl <- seu_obj$cluster_label %>% 
  table() %>% 
  enframe("cluster_label", "cluster_n") %>% 
  mutate(cluster_nrel = cluster_n/sum(cluster_n))

marker_sheet <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  group_by(cluster_label) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(rank = row_number(-avg_logFC)) %>% 
  select(cluster_label, gene, rank) %>% 
  ungroup %>% 
  mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
  spread(cluster_label, gene) %>% 
  mutate_all(.funs = helper_f)

marker_tbl_annotated <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  left_join(cluster_n_tbl, by = "cluster_label") %>% 
  select(-cluster) %>% 
  mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
  arrange(cluster_label, -avg_logFC, p_val_adj)

write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_marker_sheet.tsv"))
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/supplementary_tables/", coi, "_marker_sheet.tsv"))

write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_marker_table_annotated.tsv"))
write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/supplementary_tables/", coi, "_marker_table_annotated.tsv"))

formattable::formattable(marker_sheet)

rank	Cancer.cell.1	Cancer.cell.2	Cancer.cell.3	Cancer.cell.4	Cancer.cell.5	Cancer.cell.6	Cycling.cancer.cell.1	Cycling.cancer.cell.2	Ciliated.cell.1	Ciliated.cell.2	OV.065.specific.1	OV.075.specific	OV.081.specific
1	DAPL1	S100A9	CXCL10	COL1A1	S100A4	VEGFA	CENPF	HIST1H4C	CAPS	CFAP126	OVGP1	TAGLN	MESP1
2	SCGB2A1	KRT17	ISG15	COL3A1	CASC1	SST	PTTG1	TUBA1B	C20orf85	PIFO	SCGB3A1	TPM2	CLEC11A
3	CRABP1	LCN2	IFIT3	COL1A2	SNHG19	SLC2A1	UBE2S	PCLAF	TPPP3	FAM183A	CRISP3	LGALS1	ANPEP
4	SCGB1D2	TACSTD2	IFIT1	FN1	CETN2	BNIP3	HMGB2	ATAD2	C9orf24	DNALI1	PLCG2	ACTA2	COX4I1
5	THSD4	KRT7	MX1	SPARC	TMEM100	NDRG1	MKI67	PCNA	RSPH1	CASC1	PTMS	CALD1	NPW
6		S100A10	IFIT2	DCN	MFAP5	NDUFA4L2	TPX2	H2AFZ	C5orf49	RSPH1	CCND1	MT1H	ASRGL1
7		GPRC5A	IFI6	LUM	PEG10	FTL	ASPM	CKS1B	TMEM190	C9orf24	SNHG9	RAB32	ANXA1
8		SLPI	PARP14	IGFBP7	CCND1	P4HA1	TOP2A	TK1	PIFO	C20orf85	PEG10	NMU	CKB
9		MMP7	IFI44L	COL6A1	TTYH1	DDIT3	CCNB1	TUBB	FAM183A	LRRIQ1	DSTN	SELENOP	CFD
10		S100A11	TNFSF10	COL6A2	NAP1L1	NUPR1	CDC20	TYMS	C1orf194	C5orf49	APOLD1	CDKN2A	LINC01436
11		KRT19	GBP4	CCDC80	SELENOP	IGFBP3	CKS2	MKI67	CETN2	DNAAF1	TOP2A	MT1G	SCGB2A1
12		CRYAB	IFI27	CTHRC1	STK38L	FTH1	UBE2C	HMGB2	AGR3	TPPP3	YWHAE	ANXA3	REG1A
13		IGFBP3	STAT1	CALD1	MLF1	LDHA	ARL6IP1	DUT	CFAP126	ARMC3	EPB41L2	CCT5	NDUFA1
14		S100A6	GBP1	COL6A3	CAPS	SLC3A2	BIRC5	TOP2A	ODF3B	C1orf194	CDC20	VIM	IDH1
15		TAGLN	TNFAIP2	NNMT	LDHB	ZFAS1	LGALS1	DEK	TXN	CFAP45	RBMX	COTL1	ATP5F1A
16		ANXA1	SOD2	LGALS1	C2orf40	EGLN3	CCNB2	MCM7	CAPSL	EFCAB1	PTPRA	NDUFS6	MUC5B
17		CST6	CCL20	SPARCL1	CFAP126	ENO1	PTMS	HELLS	IGFBP7	ENKUR	MYL12B	FHL2	FBXO2
18		C19orf33	IDO1	VIM	PTMS	PLIN2	NMU	SMC4	C11orf88	ZMYND10	SNHG25	AKAP12	ATP5MC3
19		S100A2	XAF1	MMP11	LRRIQ1	ANGPTL4	TUBA1B	CDK1	MORN2	CAPSL	SNHG19	YBX1	SLC6A20
20		EMP1	CXCL8	MGP	COMP	PGK1	HMGN2	UBE2C	LRRIQ1	CEP126	PABPC4	MT1M	CYP4B1
21		ADIRF	IL32	TAGLN	SPRY1	PLOD2	CDKN3	MCM3	TUBA1A	HYDIN	AMD1	SUB1	EIF3K
22		ANXA2	OAS1	VCAN	PLTP	DDIT4	JPT1	CLSPN	FOXJ1	DYDC2	WBP11	MYL9	GSTP1
23		RARRES1	PSMB9	COL5A2	C17orf97	EIF1	NUSAP1	STMN1	TUBB4B	CDC20B	BRD9	MYH9	CSRP2
24		MUC4	HLA-B	POSTN	LMO3	ERO1A	H2AFZ	MYBL2	ZMYND10	TEKT1	METAP2	CLDN7	CNKSR3
25		MAL2	RSAD2	AEBP1	RPGR	SOX4	CEP55	UBE2T	DNAAF1	TEKT2	RBM8A	MYL12A	EPB41L2
26		LGALS3	IFIH1	ACTA2	ARMC3	FAM162A	HMGB1	MCM4	C9orf116	CFAP54	PPT1	CD47	LINC00937
27		CAST	B2M	RARRES2	PRELP	ADM	KPNA2	CENPF	EFCAB1	CFAP43	MYL6B	IL32	MFAP5
28		SOX4	RNF213	PLA2G2A	SEMA3E	EIF4EBP1	HMMR	SMC2	EFHC1	SPEF1	NMT1	EEF1B2	NPR3
29		CCND1	HLA-A	SCGB1D2	PON1	NXPH4	STMN1	NUSAP1	DNALI1	FAM81B	SNRPB	ANXA5	CTSV
30		MFGE8	TAP1	NBL1	SNHG9	TRIB3	ANP32E	NASP	IK	CFAP46	PGR	PCDH10	C2CD4B
31		TFPI2	LY6E	ERP27	HYDIN	S100A10	SMC4	HMGB1	DYDC2	ROPN1L	ST13	IL15	NACA
32		LYPD2	SAMD9	COL8A1	SPX	STC2	ECT2	RAD51AP1	CEP126	CFAP73	TTYH1	S100A10	PLEKHS1
33		TNFRSF12A	IRF1	LRRC75A	ATP5MC2	GDF15	CENPE	ZWINT	MLF1	EFCAB10	PA2G4	PFN1	RBP2
34		CAMK2N1	WARS	TIMP3	MEG3	HILPDA	NUCKS1	RRM2	SNTN	ZBBX	AP2B1	IGFBP2	TSPAN8
35		PERP	CXCL11	CDH6	PIFO	SLC16A3	CKS1B	DNMT1	ROPN1L	CFAP157	TMCO1	PDCD6	PKHD1L1
36		TGM2	DDX58	KRT19	FMOD	SQSTM1	KIF20B	E2F1	AL357093.2	DRC1	HNRNPM	KIF5B	IRX1
37		RAB11FIP1	ISG20	IGFBP4	AC004540.2	CEBPB	TROAP	TPX2	PSENEN	SPAG8	NSA2	ACTR3	GLRX
38		MUC16	LAP3	IGFBP6	NKAIN4	RNASET2	HMGB3	CENPW	CCDC170	WDR38	WDR70	EEF1D	HRASLS
39		CLDN4	OAS3	MMP2	ENKUR	GAPDH	H2AFV	RANBP1	ERICH3	WDR49	HMGB1	TPM3	AARD
40		EZR	HLA-C	KRT7	PPIL6	SEC61G	RAD21	TMPO	TEKT2	C11orf88	VIM	TAGLN2	SCGB2A2
41		SCEL	PLSCR1	TPM1	SPEF2	MALAT1	DLGAP5	MAD2L1	C9orf135	RSPH4A	TBCA	PPA1	SLC22A31
42		CTSD	IFI16	SYT8	PREX2	BNIP3L	NCAPD2	FANCI	SPA17	C7orf57	HSP90B1	PDCL2	PROM1
43		ITGB8	NFKBIA	BGN	MNS1	CA9	AURKA	CENPX	MNS1	C6orf118	CBX3	COX4I1	C16orf74
44		HEBP2	BIRC3	SPON2	FAM183A	WSB1	CENPA	TMEM106C	ARMC3	DNAH12	CST3	UQCRH	TNFRSF18
45		SVIL	CXCL2	FTH1	GRIA2	C4orf3	TUBB4B	DHFR	PERP	DYNLRB2	MT-ND4L	MUC12	MESP2
46		C15orf48	BST2	COL5A1	SLITRK6	SNHG7	CENPW	PRKDC	CIB1	ERICH3	EIF2S2	LINC02432	TNC
47		FTH1	IFI44	HTRA1	CFAP54	GPI	SKA2	HMGN2	LRRC23	WDR63	SFRP1	GPR19	PGR
48		S100A16	UBE2L6	CAV1	SPAG17	AKAP12	KIF23	ORC6	CFAP45	MORN5	ANO1	RASGRP2	PTGER2
49		UPK1B	OAS2	ANXA2	CD177	IGF2-1	TUBA1C	DTYMK	CKB	MAP3K19	TMA7	LMOD1	GLYATL2
50		FXYD3	HERC5	SERPINF1	FAM81B	ANKRD37	MAD2L1	ASPM	MORN5	TTLL9	ATP5ME	HPD	PTGFR

2 Clusters

2.1 sizes

enframe(sort(table(seu_obj$cluster_label))) %>% 
  mutate(name = ordered(name, levels = rev(name))) %>% 
  ggplot() +
  geom_bar(aes(name, value), stat = "identity") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(y = c("#cells"), x = "cluster")

2.2 UMAP

alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)

common_layers_disc <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
  labs(color = "")
)

common_layers_cont <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  scale_color_gradientn(colors = viridis(9)),
  guides(color = guide_colorbar())
)

ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  #facet_wrap(~therapy) +
  ggtitle("Sub cluster")

3 Filtering out doublet and mito high clusters

3.1 UMAP

my_subtypes <- names(clrs$cluster_label[[coi]])

my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet|specific")]

if (cell_sort == "CD45+") {
cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes & !(str_detect(seu_obj$cell_id, "CD45N"))]
}

if (cell_sort == "CD45-") {
cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes & !(str_detect(seu_obj$cell_id, "CD45P"))]
}

# seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
# seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
# seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered.rds"))

plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(cell_id = colnames(seu_obj_sub),
         cluster_label = ordered(cluster_label, levels = my_subtypes))

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  scale_color_manual(values = clrs$cluster_label[[coi]]) +
  # facet_wrap(~cluster_label) +
  ggtitle("Sub cluster")

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id_short)

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
  # geom_point(aes(umapharmony_1, umapharmony_2), 
  #            color = "grey90", size = 0.01, 
  #            data = select(plot_data_sub, -tumor_supersite)) +
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

write_tsv(select(plot_data_sub, cell_id, everything(), -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_embedding.tsv"))

3.2 add module scores and pathway scores

signature_modules_cd8 <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>% 
  gather(module, gene) %>% 
  na.omit() %>% 
  group_by(module) %>% 
  do(gene = c(.$gene)) %>% 
  {setNames(.$gene, .$module)} %>% 
  setNames(paste0(names(.), ".module"))

signature_modules_cin <- yaml::read_yaml("_data/small/signatures/cgas_sting.yaml") %>% 
  .[lapply(., length) > 1] %>% 
  setNames(paste0(names(.), ".module"))

# ## compute expression module scores
# for (i in 1:length(signature_modules_cd8)) {
#   seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules_cd8[i], name = names(signature_modules_cd8)[i])
#   seu_obj_sub[[names(signature_modules_cd8)[i]]] <- seu_obj_sub[[paste0(names(signature_modules_cd8)[i], "1")]]
#   seu_obj_sub[[paste0(names(signature_modules_cd8)[i], "1")]] <- NULL
#   print(paste(names(signature_modules_cd8)[i], "DONE"))
# }
# 
# for (i in 1:length(signature_modules_cin)) {
#   seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules_cin[i], name = names(signature_modules_cin)[i])
#   seu_obj_sub[[names(signature_modules_cin)[i]]] <- seu_obj_sub[[paste0(names(signature_modules_cin)[i], "1")]]
#   seu_obj_sub[[paste0(names(signature_modules_cin)[i], "1")]] <- NULL
#   print(paste(names(signature_modules_cin)[i], "DONE"))
# }
# 
# ## compute progeny scores
# progeny_list <- seu_obj_sub@assays$RNA@data[VariableFeatures(seu_obj_sub),] %>%
#   as.matrix %>%
#   progeny %>%
#   as.data.frame %>%
#   as.list
# 
# names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))
# 
# for (i in 1:length(progeny_list)) {
#   seu_obj_sub <- AddMetaData(seu_obj_sub, metadata = progeny_list[[i]],
#                              col.name = names(progeny_list)[i])
# }
# 
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered_annotated.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered_annotated.rds"))

3.3 marker heatmap

marker_top_tbl <- marker_sheet[,-1] %>% 
  slice(1:10) %>% 
  as.list %>% 
  .[!str_detect(names(.), "doublet")] %>% 
  enframe("cluster_label_x", "gene") %>% 
  unnest(gene)

plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>% 
  gather(gene, value, -c(1:(length(myfeatures)+1))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>% 
  group_by(cluster_label, gene) %>% 
  summarise(value = mean(value, na.rm = T)) %>% 
  group_by(gene) %>% 
  mutate(value = scales::rescale(value)) %>% 
  left_join(marker_top_tbl, by = "gene") %>% 
  mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))

ggplot(plot_data_markers) +
  geom_tile(aes(gene, cluster_label, fill = value)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  facet_grid(~cluster_label_x, scales = "free", space = "free") +
  scale_fill_gradientn(colors = viridis(9)) +
  labs(fill = "Scaled\nexpression") +
  theme(aspect.ratio = 1,
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank())

# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)

3.4 composition

3.4.1 per site

comp_site_tbl <- plot_data_sub %>%
  filter(!is.na(tumor_supersite)) %>% 
  group_by(cluster_label, tumor_supersite) %>%
  tally %>%
  group_by(tumor_supersite) %>%
  mutate(nrel = n/sum(n)*100) %>%
  ungroup

pnrel_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  scale_fill_manual(values = clrs$cluster_label[[coi]]) +
  labs(fill = "Cluster", y = "Fraction [%]", x = "")

pnabs_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, n, fill = cluster_label),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  scale_fill_manual(values = clrs$cluster_label[[coi]]) +
  labs(fill = "Cluster", y = "# cells", x = "") 

plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")

# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)

3.4.2 per sample

comp_tbl_sample_sort <- plot_data_sub %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>% 
  tally %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>% 
  mutate(nrel = n/sum(n)*100,
         nsum = sum(n),
         log10n = log10(n),
         label_supersite = "Site",
         label_mutsig = "Signature",
         label_therapy = "Rx") %>% 
  ungroup %>% 
  arrange(desc(therapy), tumor_supersite) %>% 
  mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>% 
  mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
         tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>% 
  arrange(patient_id) %>% 
  mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))

sample_id_x_tbl <- plot_data_sub %>% 
  mutate(sort_short_x = cell_sort) %>% 
  distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>% 
  arrange(sample_id_x)

comp_tbl_sample_sort %>% 
  mutate(sort_short_x = cell_sort) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>% 
  select(sample_id_x, cluster_label, n, nrel, nsum) %>% 
  left_join(sample_id_x_tbl, by = "sample_id_x") %>% 
  write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_subtype_compositions.tsv"))

ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)

max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]

comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
  if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
  if (y == "n") ylab <- paste0("Number\nof cells")
  p <- ggplot(comp_tbl_sample_sort, 
              aes_string("tumor_subsite_rx", y, fill = "cluster_label")) + 
    facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
    coord_cartesian(clip = "off") + 
    scale_fill_manual(values = clrs$cluster_label[[coi]]) + 
    theme(axis.text.x = element_blank(),
          axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5, 
                                      margin = margin(0, -0.4, 0, 0, unit = "npc")),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.line.x = element_blank(),
          strip.text.y = element_blank(),
          strip.text.x = element_blank(),
          strip.background.y = element_blank(),
          strip.background.x = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) + 
    labs(x = "",
         y = ylab) +
    guides(fill = FALSE)
  if (y == "nrel") p <- p + 
    geom_bar(stat = "identity") +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, 50, 100), 
                       labels = c("0", "50", "100"))
  if (y == "n") p <- p + 
    geom_bar(stat = "identity", position = position_stack()) +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, ymaxn/2, ymaxn),
                       limits = c(0, ymaxn),
                       labels = c("", ymaxn/2, ymaxn)) +
    expand_limits(y = c(0, ymaxn)) +
    theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
  return(p)
} 

common_label_layers <- list(
  geom_tile(color = "white", size = 0.15),
  theme(axis.text.x = element_blank(),
        axis.ticks = element_blank(),
        axis.title.x = element_blank(),
        axis.line.x = element_blank(),
        strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc")),
  scale_y_discrete(expand = c(0, 0)),
  labs(y = ""),
  guides(fill = FALSE),
  facet_grid(~patient_id, 
             space = "free", scales = "free")
)

comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id), 
       aes(tumor_subsite_rx, label_supersite, 
           fill = tumor_supersite)) + 
  scale_fill_manual(values = clrs$tumor_supersite) +
  common_label_layers

comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_therapy, 
           fill = therapy)) + 
  scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
  common_label_layers

comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_mutsig, 
           fill = consensus_signature)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  common_label_layers

patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)

comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
  facet_grid(~patient_id, 
             space = "free", scales = "free") +
  coord_cartesian(clip = "off") + 
  theme_void() +
  theme(strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc"))

hist_plot_wrapper <- function(x = "nrel") {
  if (x == "nrel") {
    xlab <- paste0("Fraction of cells [%]")
    bw <- 5
  }
  if (x == "log10n") {
    xlab <- paste0("Number of cells")
    bw <- 0.2
  }
  p <- ggplot(comp_tbl_sample_sort) +
    ggridges::geom_density_ridges(
      aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
      stat = "binline", binwidth = bw, scale = 3) +
    facet_grid(label_supersite~., 
               space = "free", scales = "free") +
    scale_fill_manual(values = clrs$cluster_label[[coi]]) + 
    theme(axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.title.y = element_blank(),
          axis.line.y = element_blank(),
          strip.text = element_blank(),
          strip.background = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_discrete(expand = c(0, 0)) +
    guides(fill = FALSE) +
    labs(x = xlab)
  if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) + 
    scale_x_continuous(expand = c(0, 0), 
                       labels = function(x) parse(text = paste("10^", x)))
  return(p)
}

pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")

pcomp_grid <- plot_grid(comp_label_patient_id, 
                        pcomp1, pcomp2, 
                        comp_label_site, comp_label_rx, comp_label_mutsig,
                        ncol = 1, align = "v", 
                        rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))

phist1 <- hist_plot_wrapper("log10n")

pcomp_hist_grid <- ggdraw() +
  draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
  draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)

pcomp_hist_grid

# ggsave(paste0("_fig/02_composition_v7_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)

3.4.3 site specific cluster enrichment

comp_tbl_z <- comp_tbl_sample_sort %>% 
  filter(therapy == "pre-Rx",
         !(tumor_supersite %in% c("Ascites", "Other"))) %>% 
  group_by(patient_id, cluster_label) %>% 
  arrange(patient_id, cluster_label, nrel) %>% 
  mutate(rank = row_number(nrel),
         z_rank = scales::rescale(rank)) %>% 
  mutate(mean_nrel = mean(nrel, na.rm = T),
         sd_nrel = sd(nrel, na.rm = T),
         z_nrel = (nrel - mean_nrel) / sd_nrel) %>% 
  ungroup()

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

4 session info

devtools::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.6.2 (2019-12-12)
##  os       Debian GNU/Linux 10 (buster)
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       Etc/UTC                     
##  date     2021-03-09                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version    date       lib
##  ape            5.3        2019-03-17 [2]
##  assertthat     0.2.1      2019-03-21 [2]
##  backports      1.1.10     2020-09-15 [1]
##  bibtex         0.4.2.2    2020-01-02 [2]
##  Biobase        2.46.0     2019-10-29 [2]
##  BiocGenerics   0.32.0     2019-10-29 [2]
##  bitops         1.0-6      2013-08-17 [2]
##  broom          0.7.2      2020-10-20 [1]
##  callr          3.4.2      2020-02-12 [1]
##  caTools        1.17.1.4   2020-01-13 [2]
##  cellranger     1.1.0      2016-07-27 [2]
##  cli            2.0.2      2020-02-28 [1]
##  cluster        2.1.0      2019-06-19 [3]
##  codetools      0.2-16     2018-12-24 [3]
##  colorblindr  * 0.1.0      2020-01-13 [2]
##  colorspace   * 1.4-2      2019-12-29 [2]
##  cowplot      * 1.0.0      2019-07-11 [2]
##  crayon         1.3.4      2017-09-16 [1]
##  data.table     1.12.8     2019-12-09 [2]
##  DBI            1.1.0      2019-12-15 [2]
##  dbplyr         2.0.0      2020-11-03 [1]
##  desc           1.2.0      2018-05-01 [2]
##  devtools       2.2.1      2019-09-24 [2]
##  digest         0.6.25     2020-02-23 [1]
##  dplyr        * 1.0.2      2020-08-18 [1]
##  ellipsis       0.3.1      2020-05-15 [1]
##  evaluate       0.14       2019-05-28 [2]
##  fansi          0.4.1      2020-01-08 [2]
##  farver         2.0.3      2020-01-16 [1]
##  fitdistrplus   1.0-14     2019-01-23 [2]
##  forcats      * 0.5.0      2020-03-01 [1]
##  formattable    0.2.0.1    2016-08-05 [1]
##  fs             1.5.0      2020-07-31 [1]
##  future         1.15.1     2019-11-25 [2]
##  future.apply   1.4.0      2020-01-07 [2]
##  gbRd           0.4-11     2012-10-01 [2]
##  gdata          2.18.0     2017-06-06 [2]
##  generics       0.0.2      2018-11-29 [2]
##  ggplot2      * 3.3.2      2020-06-19 [1]
##  ggrepel        0.8.1      2019-05-07 [2]
##  ggridges       0.5.2      2020-01-12 [2]
##  globals        0.12.5     2019-12-07 [2]
##  glue           1.3.2      2020-03-12 [1]
##  gplots         3.0.1.2    2020-01-11 [2]
##  gridExtra      2.3        2017-09-09 [2]
##  gtable         0.3.0      2019-03-25 [2]
##  gtools         3.8.1      2018-06-26 [2]
##  haven          2.3.1      2020-06-01 [1]
##  hms            0.5.3      2020-01-08 [1]
##  htmltools      0.5.1.1    2021-01-22 [1]
##  htmlwidgets    1.5.1      2019-10-08 [2]
##  httr           1.4.2      2020-07-20 [1]
##  ica            1.0-2      2018-05-24 [2]
##  igraph         1.2.5      2020-03-19 [1]
##  irlba          2.3.3      2019-02-05 [2]
##  jsonlite       1.7.1      2020-09-07 [1]
##  KernSmooth     2.23-16    2019-10-15 [3]
##  knitr          1.26       2019-11-12 [2]
##  labeling       0.3        2014-08-23 [2]
##  lattice        0.20-38    2018-11-04 [3]
##  lazyeval       0.2.2      2019-03-15 [2]
##  leiden         0.3.1      2019-07-23 [2]
##  lifecycle      0.2.0      2020-03-06 [1]
##  listenv        0.8.0      2019-12-05 [2]
##  lmtest         0.9-37     2019-04-30 [2]
##  lsei           1.2-0      2017-10-23 [2]
##  lubridate      1.7.9.2    2020-11-13 [1]
##  magrittr     * 2.0.1      2020-11-17 [1]
##  MASS           7.3-51.5   2019-12-20 [3]
##  Matrix         1.2-18     2019-11-27 [3]
##  memoise        1.1.0      2017-04-21 [2]
##  metap          1.2        2019-12-08 [2]
##  mnormt         1.5-5      2016-10-15 [2]
##  modelr         0.1.8      2020-05-19 [1]
##  multcomp       1.4-12     2020-01-10 [2]
##  multtest       2.42.0     2019-10-29 [2]
##  munsell        0.5.0      2018-06-12 [2]
##  mutoss         0.1-12     2017-12-04 [2]
##  mvtnorm        1.0-12     2020-01-09 [2]
##  nlme           3.1-143    2019-12-10 [3]
##  npsurv         0.4-0      2017-10-14 [2]
##  numDeriv       2016.8-1.1 2019-06-06 [2]
##  pbapply        1.4-2      2019-08-31 [2]
##  pillar         1.4.6      2020-07-10 [1]
##  pkgbuild       1.0.6      2019-10-09 [2]
##  pkgconfig      2.0.3      2019-09-22 [1]
##  pkgload        1.0.2      2018-10-29 [2]
##  plotly         4.9.1      2019-11-07 [2]
##  plotrix        3.7-7      2019-12-05 [2]
##  plyr           1.8.5      2019-12-10 [2]
##  png            0.1-7      2013-12-03 [2]
##  prettyunits    1.1.1      2020-01-24 [1]
##  processx       3.4.2      2020-02-09 [1]
##  progeny      * 1.11.3     2020-10-22 [1]
##  ps             1.3.2      2020-02-13 [1]
##  purrr        * 0.3.4      2020-04-17 [1]
##  R.methodsS3    1.7.1      2016-02-16 [2]
##  R.oo           1.23.0     2019-11-03 [2]
##  R.utils        2.9.2      2019-12-08 [2]
##  R6             2.4.1      2019-11-12 [1]
##  RANN           2.6.1      2019-01-08 [2]
##  rappdirs       0.3.1      2016-03-28 [2]
##  RColorBrewer   1.1-2      2014-12-07 [2]
##  Rcpp           1.0.4      2020-03-17 [1]
##  RcppAnnoy      0.0.16     2020-03-08 [1]
##  RcppParallel   4.4.4      2019-09-27 [2]
##  Rdpack         0.11-1     2019-12-14 [2]
##  readr        * 1.4.0      2020-10-05 [1]
##  readxl       * 1.3.1      2019-03-13 [2]
##  rematch        1.0.1      2016-04-21 [2]
##  remotes        2.1.0      2019-06-24 [2]
##  reprex         0.3.0      2019-05-16 [2]
##  reshape2       1.4.3      2017-12-11 [2]
##  reticulate     1.14       2019-12-17 [2]
##  rlang          0.4.8      2020-10-08 [1]
##  rmarkdown      2.0        2019-12-12 [2]
##  ROCR           1.0-7      2015-03-26 [2]
##  rprojroot      1.3-2      2018-01-03 [2]
##  rstudioapi     0.11       2020-02-07 [1]
##  rsvd           1.0.3      2020-02-17 [1]
##  Rtsne          0.15       2018-11-10 [2]
##  rvest          0.3.6      2020-07-25 [1]
##  sandwich       2.5-1      2019-04-06 [2]
##  scales         1.1.0      2019-11-18 [2]
##  sctransform    0.2.1      2019-12-17 [2]
##  SDMTools       1.1-221.2  2019-11-30 [2]
##  sessioninfo    1.1.1      2018-11-05 [2]
##  Seurat       * 3.1.2      2019-12-12 [2]
##  sn             1.5-4      2019-05-14 [2]
##  stringi        1.5.3      2020-09-09 [1]
##  stringr      * 1.4.0      2019-02-10 [1]
##  survival       3.1-8      2019-12-03 [3]
##  testthat       2.3.2      2020-03-02 [1]
##  TFisher        0.2.0      2018-03-21 [2]
##  TH.data        1.0-10     2019-01-21 [2]
##  tibble       * 3.0.4      2020-10-12 [1]
##  tidyr        * 1.1.2      2020-08-27 [1]
##  tidyselect     1.1.0      2020-05-11 [1]
##  tidyverse    * 1.3.0      2019-11-21 [2]
##  tsne           0.1-3      2016-07-15 [2]
##  usethis        1.5.1      2019-07-04 [2]
##  uwot           0.1.5      2019-12-04 [2]
##  vctrs          0.3.5      2020-11-17 [1]
##  viridis      * 0.5.1      2018-03-29 [2]
##  viridisLite  * 0.3.0      2018-02-01 [2]
##  withr          2.3.0      2020-09-22 [1]
##  xfun           0.12       2020-01-13 [2]
##  xml2           1.3.2      2020-04-23 [1]
##  yaml           2.2.1      2020-02-01 [1]
##  zoo            1.8-7      2020-01-10 [2]
##  source                                 
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## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library

MSK SPECTRUM freeze major subset deep dives

Florian Uhlitz

2021-03-09

1 Cluster markers

1.1 Major Ovarian.cancer.super markers for cell assign

1.2 Subtype currated markers

1.3 Subtype cluster markers

2 Clusters

2.1 sizes

2.2 UMAP

3 Filtering out doublet and mito high clusters

3.1 UMAP

3.2 add module scores and pathway scores

3.3 marker heatmap

3.4 composition

3.4.1 per site

3.4.2 per sample

3.4.3 site specific cluster enrichment

4 session info